What are Convolutional Neural Networks (CNNs)?

Artificial
intelligence
has
reached
a
position
that
would
be
impossible
to
imagine
a
few
years
back.

Machine
learning
models
are
capable
of
producing
output
that
would
have
required
months
of
work
from
expert
data
scientists.
Artificial
intelligence
technology
has
helped
in
achieving
significant
wonders
that
would
be
impossible
for
human
beings.
You
might
wonder
why
you
should
focus
on
questions
like
“What
is
convolutional
neural
networks
(CNNs)?”
in
discussions
about
artificial
intelligence
and
ML.

The
most
important
reason
to
focus
on
convolutional
neural
networks
in
AI
is
the
limitations
of
AI
in
image
processing.
AI
models
have
not
proved
as
effective
as
the
human
brain
in
recognizing
and
processing
images.
The
human
brain
works
in
mysterious
and
complex
ways.
You
could
not
find
a
definitive
way
to
understand
the
cognition
and
rendering
mechanisms
of
the
brain.
You
can
find
different
layers
of
interconnected
neurons
in
the
human
brain.
Interestingly,
AI
can
replicate
the
structure
of
your
brain
with
the
help
of
artificial
neurons.

In
the
mid-20s,

artificial
neural
networks
gained
momentum
as
they
offered
capabilities
for
learning
from
data
in
a
theoretical
manner.
However,
artificial
neural
networks
had
to
evolve
into
convolutional
neural
networks.
It
was
a
vital
necessity
to
adapt
to
the
use
cases
of
image
recognition
and
processing.
Let
us
learn
more
about
convolutional
neural
networks
and
how
they
work.

What
is
a
Convolutional
Neural
Network
or
CNN?

Neural
networks
are
an
important
subset
in
the
domain
of
machine
learning.
You
can
understand
convolutional
neural
networks
(CNNs)
as
an
important
component
in
the
world
of

deep
learning
algorithms.
Neural
networks
include
node
layers
featuring
an
input
layer
alongside
one
or
multiple
hidden
layers
and
an
output
layer.
When
the
output
of
individual
nodes
exceeds
a
specific
threshold
value,
the
node
starts
working
and
sends
data
to
the
next
layer
in
the
network.
Without
such
procedures,
it
is
impossible
to
pass
data
along
to
the
next
layer
in
the
network.

You
can
come
across
different
types
of
neural
networks
that
are
suitable
for
different
data
types
and
use
cases.
For
example,
recurrent
neural
networks
are
generally
used
in
speech
recognition
and

natural
language
processing
tasks.
On
the
other
hand,
a
convolutional
neural
network
example
would
show
that
CNNs
are
generally
used
for
computer
vision
tasks.
CNNs
can
also
support
image
classification
tasks.
Before
the
arrival
of
convolutional
neural
networks,
the
identification
of
objects
in
images
involved
time-intensive
methods
for
feature
extraction.

Convolutional
neural
networks
offer
a
scalable
approach
for
object
recognition
and
image
classification
tasks.
CNNs
utilize
linear
algebra
principles,
such
as
matrix
multiplication,
for
effective
identification
of
patterns
in
an
image.
However,
the
working
of
CNNs
may
demand
excessive
use
of
computational
resources,
such
as
graphical
processing
units
or
GPUs.

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Working
Mechanism
of
Convolutional
Neural
Networks

Another
important
highlight
in
a
guide
to
learn
convolutional
neural
networks
(CNNs)
is
the
working
mechanism
of
CNNs.
You
can
learn
more
about
the
workings
of
CNNs
by
uncovering
information
about
their
architecture.
Convolutional
neural
networks
are
different
from
other
neural
networks
in
that
they
have
superior
performance
for
image,
audio,
and
speech
signal
inputs.
The
architecture
of
convolutional
neural
networks
includes
three
layers:
a
convolutional
layer,
a
pooling
layer,
and
a
fully-connected
layer.

It
is
important
to
note
that
the
CNN
becomes
more
complex
with
every
layer
and
identifies
larger
portions
in
the
image.
The
initial
layers
emphasize
simple
features,
such
as
edges
and
colors.
When
the
image
data
moves
through
different
layers
of
the
CNN,
it
recognizes
the
larger
elements
or
shapes
of
objects
until
it
identifies
the
desired
object.
Here
is
an
outline
of
the
different
ways
in
which
the
different
layers
in
CNNs
work.

• Convolutional
  Layer

The
most
integral
component
of
a
convolutional
neural
network
is
the
convolutional
network.
The
working
mechanism
of
convolutional
neural
networks
explained
to
beginners,
revolves
around
the
convolutional
layer,
which
is
the
core
building
block
of
CNNs.
It
is
the
site
where
most
of
the
computation
works
in
a
convolutional
neural
network.

Some
of
the
notable
components
in
the
convolutional
layer
include
input
data,
feature
maps,
and
filters.
For
example,
the
input
for
the
convolutional
layer
can
be
a
color
image
that
includes
a
matrix
of
3D
pixels.
Therefore,
the
input
would
have
three
different
dimensions,
height,
depth,
and
width,
which
correlate
with
the
RGB
in
the
image.

Convolutional
layer
also
includes
a
feature
detector
or
a
filter.
Also
known
as
kernel,
the
feature
detector
would
traverse
through
receptive
areas
of
the
image
to
check
for
a
feature.
The
process
that
helps
in
recognition
of
objects
in
images
is
convolution.

The
feature
detector
in
the
convolutional
layer
of
CNNs
is
a
two-dimensional
array
of
weights
that
represents
different
parts
of
the
image.
Although
the
array
can
vary
in
size,
the
filter
size
is
generally
a
3×3
matrix,
which
also
determines
the
size
of
the
receptive
field.
The
filter
is
implemented
on
an
area
of
the
image,
followed
by
calculation
of
a
dot
product
between
the
filter
and
the
input
pixels.

You
can
then
feed
the
dot
product
into
an
output
array.
Subsequently,
the
filter
would
shift
by
a
huge
margin
and
repeat
the
process
until
the
kernel
covers
the
complete
image.
The
final
output
obtained
from
the
sequence
of
dot
products
from
the
input
and
filter
serves
as
the
feature
map
or
activation
map.

The
description
of
convolutional
neural
network
introduction
also
emphasizes
the
fact
that
weights
in
the
feature
detector
are
fixed
during
movement
through
the
image.
Parameter
sharing
enables
consistency
in
the
weights
of
feature
detector.
However,
some
of
the
parameters,
such
as
weight
values,
can
vary
during
the
course
of
training
through
gradient
descent
and
backpropagation.

On
the
other
hand,
you
can
find
three
hyper-parameters
that
influence
the
volume
size
of
the
output
that
you
must
set
prior
to
the
training
of
neural
networks.
The
three
hyper-parameters
include
a
number
of
filters,
stride,
and
zero-padding.

• Additional
  Convolutional
  Layer

The
architecture
of
convolutional
neural
network
also
includes
an
additional
convolutional
layer
other
than
initial
convolution
layer.
It
is
an
important
tool
in
convolutional
neural
network
applications
as
the
additional
convolutional
layer
follows
the
first
convolutional
layer.

The
structure
of
the
convolutional
neural
network
becomes
hierarchical,
and
later
layers
can
witness
the
pixels
in
receptive
fields
of
previous
layers.
Finally,
the
convolutional
layer
transforms
the
image
to
numerical
values,
thereby
helping
the
neural
network
in
interpretation
and
extraction
of
relevant
patterns.

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• Pooling
  Layer

The
next
important
layer
in
the
architecture
of
CNNs
refers
to
pooling
layers
or
downsampling.
It
helps
in
conducting
dimensionality
reduction
alongside
reducing
the
number
of
parameters
in
the
input.
Just
like
the
convolutional
layer,
the
pooling
layer
would
pass
a
filter
throughout
the
complete
input.
However,
the
filter
in
the
pooling
layer
does
not
rely
on
weights.
On
the
contrary,
the
kernel
utilizes
an
aggregation
function
to
the
values
in
the
receptive
field,
thereby
filling
the
output
array.

The
review
of
pooling
layer
in
the
convolutional
neural
networks
(CNNs)
shows
that
you
can
utilize
two
variants
of
pooling.
You
can
find
two
types
of
pooling
when
working
with
convolutional
neural
networks:
average
pooling
and
max
pooling.
When
the
filter
moves
through
the
input,
it
can
choose
the
pixel
with
the
maximum
value
for
transferring
to
the
output
array
in
max
pooling.

Interestingly,
max
pooling
is
a
commonly
used
approach
in
the
pooling
layer.
You
can
understand
the
mechanisms
of
average
pooling
when
the
filter
moves
through
the
input
and
determines
the
average
value
in
the
receptive
field
that
it
should
send
to
the
output
array.
The
pooling
layer
is
the
site
in
a
CNN
where
it
loses
a
lot
of
information.
However,
it
presents
multiple
advantages,
including
reduction
of
complexity,
reduced
risks
of
overfitting,
and
improved
efficiency.

• Fully
  Connected
  Layer

The
final
addition
among
the
components
in
the
architecture
of
convolutional
layer
networks
is
the
fully-connected
layer.
As
the
name
implies,
it
serves
as
a
comprehensively
connected
layer
in
the
CNNs.
One
of
the
important
aspects
of
a
convolutional
neural
network
introduction
points
to
how
the
pixel
values
of
the
input
images
do
not
have
a
direct
connection
to
output
layer
in
the
partially
connected
layers.
On
the
other
hand,
a
fully
connected
layer
has
every
node
in
the
output
layer
connecting
directly
to
another
node
in
a
previous
layer.

The
fully
connected
layer
addresses
the
requirements
of
tasks,
such
as
classification
according
to
features
identified
through
previous
layers
and
different
filters.
Convolutional
and
pooling
layers
generally
use
ReLu
functions,
and
fully
connected
layers
rely
on
a
softmax
activation
function
for
appropriate
classification
of
inputs.

Differences
between
CNNs
and
Traditional
Neural
Networks

The
rise
of
convolutional
neural
networks
has
become
a
formidable
highlight
in
the
domain
of
AI.
However,
it
is
important
to
review
every
convolutional
neural
network
example
from
the
perspective
of
previous
traditional
neural
networks.
Traditional
neural
networks,
such
as
multilayer
perceptrons,
include
different
types
of
fully
connected
layers.
The
neural
networks
can
be
versatile,
albeit
without
optimization
for
spatial
data
such
as
images.
It
can
create
different
types
of
problems
when
used
to
manage
larger
and
more
complex
input
data.

In
the
case
of
smaller
images
with
limited
color
channels,
traditional
neural
networks
can
produce
satisfactory
results.
However,
the
increasing
image
size
and
complexity
can
lead
to
the
requirement
of
computational
resources.
In
addition,
traditional
neural
networks
experience
concerns
of
overfitting
because
fully
connected
architectures
do
not
prioritize
the
relevant
features
automatically.
Convolutional
neural
networks
are
different
in
many
ways.

First
of
all,
the
fundamentals
of
convolutional
neural
networks
explained
the
ways
in
which
every
node
is
not
connected
to
all
nodes
in
the
next
layer.
Convolutional
layers
have
limited
parameters
as
compared
to
fully
connected
layers
in
traditional
neural
networks.
Therefore,
CNNs
can
perform
image
processing
tasks
with
better
efficiency.

Convolutional
neural
networks
utilize
the
parameter-sharing
technique
for
efficiency
in
management
of
image
data.
The
convolutional
layers
work
with
the
same
filter
for
scanning
the
complete
image,
thereby
reducing
the
number
of
parameters.
Subsequently,
the
pooling
layers
also
present
promising
advantages
by
reducing
dimensionality
of
the
data
to
improve
overall
generalization
and
efficiency
of
a
convolutional
neural
network.

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How
Can
Convolutional
Neural
Networks
Support
Deep
Learning?

Deep
learning
is
a
prominent
subdomain
of
machine
learning
and
utilizes
neural
networks
with
multiple
layers
to
achieve
benefits
that
are
impossible
to
achieve
with
single-layer
networks.
Convolutional
neural
networks
are
a
prominent
variant
of
deep
learning
algorithms.
More
people
want
to
learn
convolutional
neural
networks
(CNNs)
because
they
are
the
best
tools
for
computer
vision
tasks,
including
image
classification
and
object
recognition.
CNNs
are
designed
to
learn
the
spatial
hierarchies
of
features
in
an
image
by
capturing
the
essential
features
in
early
layers
alongside
complex
patterns
in
the
deeper
layers.

The
most
significant
benefit
of
CNN
for
deep
learning
is
the
ability
to
perform
feature
learning
and
automatic
feature
extraction.
It
eliminates
the
necessity
of
extracting
features
through
manual
measures
that
involve
intensive
use
of
labor
and
complex
processes.
The
review
of
convolutional
neural
network
applications
also
shows
that
they
are
useful
for
transfer
learning.
In
this
approach,
you
can
fine-tune
a
pre-trained
model
for
new
tasks.

The
reusability
ensures
that
CNNs
are
efficient
in
dealing
with
a
versatile
range
of
tasks
with
limitations
on
training
data.
ML
developers
can
use
CNNs
in
different
real-world
scenarios
without
building
up
computational
costs.
Therefore,
convolutional
neural
networks
can
serve
as
a
valuable
resource
in
different
sectors
such
as
retail,
healthcare,
social
media,
and
automotive
industry.

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Final
Words

The
applications
of
convolutional
neural
networks
(CNNs)
spelled
new
benchmarks
for
transformation
of
conventional
perspectives
regarding
usability
of
AI
models.
You
can
notice
how
the
structure
of
CNNs
helps
them
address
the
tasks
of
image
classification
and
object
detection.
For
example,
convolutional
neural
networks
can
serve
the
healthcare
sector
with
enhancements
in
medical
imaging
and
diagnostics.

The
architecture
of
CNNs
is
different
from
traditional
neural
networks
and
ensures
optimal
use
of
resources.
As
you
unravel
the
new
perspectives
on
usability
of
CNNs
in
different
sectors,
you
can
discover
more
about
their
potential.
Learn
more
about
the
different
types
of
convolutional
neural
networks
and
how
they
can
transform
the
usability
of

artificial
intelligence
and

machine
learning
models.